goods distribution with electric vehicles: review and

Goods Distribution with Electric Vehicles: Review and Research Perspectives Samuel Pelletier Ola Jabali Gilbert Laporte September 2014

CIRRELT-2014-44

Goods Distribution with Electric Vehicles: Review and

Research Perspectives

Samuel Pelletier1,2, Ola Jabali1,3,*, Gilbert Laporte1,2

1 Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) 2 Department of Management Sciences, HEC Montréal, 3000 chemin de la Côte-Sainte-Catherine,

Montréal, Canada H3T 2A7 3 Department of Logistics and Operations Management, HEC Montréal, 3000 Côte-Sainte-

Catherine, Montréal, Canada H3T 2A7

Abstract. Over the past decade, electric vehicles have gained in popularity in several

countries, even though their market share is still relatively low. However, most gains have

been made in the area of passenger vehicles and most technical and scientific studies

have been devoted to this case. In contrast, the potential of electric vehicular technology

for goods distribution has received less attention. The issues related to the use of EVs for

goods distribution open up a wide range of relevant research problems. The aims of this

survey paper are to provide transportation researchers an overview of the technical and

marketing background needed to conduct research in this area, to present a survey of the

existing research in this field, and to offer perspectives for future research.

Keywords: Electric vehicular technology, batteries, market penetration, profitability,

incentives, green transportation, city logistics.

Acknowledgements. This research was partly supported by the Natural Sciences and

Engineering Research Council of Canada (NSERC) under grants 463834-2014, 436014-

2013 and 39682-10. This support is gratefully acknowledged.

Results and views expressed in this publication are the sole responsibility of the authors and do not necessarily reflect those of CIRRELT.

Les résultats et opinions contenus dans cette publication ne reflètent pas nécessairement la position du CIRRELT et n'engagent pas sa responsabilité.

_____________________________ * Corresponding author: [email protected] Dépôt légal – Bibliothèque et Archives nationales du Québec Bibliothèque et Archives Canada, 2014

© Pelletier, Jabali, Laporte and CIRRELT, 2014

1. Introduction

Green logistics encompasses the set of practices aimed at controlling environmental exter-

nalities in logistics operations (Psaraftis, 2014). The study of this relatively new field has

increased in popularity in recent years, as witnessed by the growing number of publications

on green transportation (Demir et al., 2014) and city logistics (Gonzalez-Feliu et al., 2014).

Research on green transportation has focused on the pollution-routing problem (Bektas and

Laporte, 2011) which seeks to design optimal vehicle routes in which routing costs and pol-

luting emissions are jointly minimized. City logistics proposes ways of reducing pollution

in urban environments, such as forbidding or restricting the use of heavy vehicles in city

centers. The research carried out in these fields focuses on the design of distribution policies

that will help diminish the environmental impact of goods distribution. However, relatively

few papers have been published on vehicle fleet composition in such a context, although

there are some recent interesting exceptions (e.g., Kopfer and Kopfer, 2013; Kopfer et al.,

2014; Koc et al., 2014). In particular, little attention has been paid to the use of electric

vehicles (EVs) as a means to yield green distribution practices.

The market share of electric cars is insignificant in most countries, with the possible

exception of Norway and the Netherlands (Mock and Yang (2014)), and electric vans and

trucks are even rarer because of several real or perceived factors related to cost, driving

range, speed and reliability. Electric trucks operate on batteries that have a limited lives

and are characterized by high costs as well as specific degradation and charging patterns

which have to be taken into account in fleet size and mix decisions. In addition, because the

number of electric recharging stations is still very small compared with that of conventional

fuel stations, the limited range of EVs becomes a critical constraint in purchasing and op-

erational decisions. However, when coupled with interesting incentives, EVs can sometimes

become an interesting option for goods distribution. As Taefi et al. (2014, p.3) point out,

“many initiatives and involvement in EV experiments are driven by companies awareness

and anticipation of regulations for less environmentally-friendly vehicles becoming more re-

strictive in the future”. The deployment of electric freight vehicles should therefore not be

automatically associated with profitability, but rather with exploration and preparation in

anticipation of expected future developments.

Several project reports provide examples of practices with electric vehicles for goods dis-

tribution, most of these in Europe. In North America, large companies using battery electric

delivery vehicles include FedEx, General Electric, Coca-Cola, UPS, Frito-Lay, Staples, En-

terprise, Hertz and others (Electrification Coalition (2013)). Many U.S. companies operating

Goods Distribution with Electric Vehicles: Review and Research Perspectives

CIRRELT-2014-44 1

battery electric trucks received funding from the American Recovery and Reinvestment Act

to cover part of the vehicles purchase costs (US DOE (2013b)). One of project FREVUE’s

reports (Nesterova et al. (2013)) provides overviews of European companies and projects

using these vehicles. The same report also points out to two-phase delivery as being an

interesting logistics concept for electric freight vehicles, where goods are first brought to a

logistics facility near the denser urban area by conventional trucks before being transferred

to the electric vehicles for last mile deliveries. When multiple organisations use the same

facility, this is known as an urban consolidation centre (UCC), defined as “a logistics facility

situated in relatively close proximity to the geographic area that it serves (...), to which

many logistics companies deliver goods destined for the area, from which consolidated de-

liveries are carried out within that area, in which a range of other value-added logistics and

retail services can be provided”(Allen et al. (2007, p.61)). Examples of a UCC using battery

electric vehicles can be found in Vermie (2002), Allen et al. (2007), van Duin et al. (2010),

TU Delft et al. (2013) and Nesterova et al. (2013).

The issues related to the use of EVs for goods distribution open up a wide range of

relevant research problems. The aims of this paper are to provide transportation researchers

an overview of the technical background and market penetration information needed to

conduct research in this area, to present a survey of the existing research in this field, and

to offer perspectives for future research. It is organized as follows. Section 2 describes the

vehicle technology, including vehicle types and batteries. Section 3 provides information on

the market penetration of EVs as well as their cost competitiveness and adoption incentives.

Section 4 provides an overview of the existing research on EVs in transportation science.

It covers three topics: fleet size and mix, vehicle routing and optimal paths. Section 5

suggests several promising research perspectives. It is organized around strategic, tactical

and operational transportation science issues. The paper closes with some conclusions in

Section 6.

2. Technological background

This section provides a classification of electric vehicle technology under three main headings:

electric vehicle types, batteries and battery recharging.


2 CIRRELT-2014-44

2.1 Electric vehicle types

Electric vehicles are generally divided into Battery Electric Vehicles (BEVs), Hybrid Electric

Vehicles (HEVs), and Fuel Cell Electric Vehicles (FCEVs). An advantage of BEVs and HEVs

is their ability to use their electric motor for regenerative breaking (to recuperate kinetic

energy) and as a form of frictionless breaking (Larminie and Lowry (2003)). FCEVs can

also allow for regenerative breaking if a battery is used to assist the fuel cell (Emadi et al.

(2005)).

2.1.1 Battery electric vehicles

A BEV is propelled by one or more electric motors and only uses the power provided by

its on-board battery for propulsion (Electrification Coalition (2013)). The large battery is

charged from the electricity grid and the powertrain is very simple by design (Tuttle and

Kockelman (2012)). Benefits include the absence of tailpipe emissions, high efficiency, as

well as much less operating noise; technical disadvantages include a relatively low achievable

range and the long time required for charging the batteries because of their low energy density

(Pollet et al. (2012)). Their motors can produce great torque at low speeds (MacLean and

Lave (2003)) and are generally three times more efficient than internal combustion engines

(de Santiago et al.(2012)). BEVs have much less moving parts than internal combustion

engine vehicles (ICEVs) and do not require regular oil changes (Feng and Figliozzi (2013)).

The regenerative breaking also allows for less break wear, further reducing maintenance

costs (Lee et al. (2013)). Typical ranges vary from 100 to 150 kilometers on a single charge

for freight BEVs (Nesterova et al. (2013)) and have been reported to diminish over time

due to battery ageing (Taefi et al. (2014)). Other factors which temporarily reduce vehicle

range include extreme temperatures, high driving speeds, rapid acceleration, carrying heavy

loads and driving up important slopes (US DOE (2012b)). Battery size and weight reduce

maximum payload for freight BEVs compared to equivalent ICEVs (den Boer et al. (2013)).

2.1.2 Hybrid electric vehicles

Hybrid electric vehicles can be classified according to their powertrain architecture (series,

parallel, series-parallel, complex), the level of electric power and function of the electric motor

(micro hybrid, mild hybrid, full hybrid) or their capacity to be plugged into an electricity

grid to recharge their batteries (Chan (2007)).

A Plug-in Hybrid Electric Vehicle (PHEV) is essentially a hybrid electric vehicle with a


CIRRELT-2014-44 3

larger battery which can be charged from the electricity grid (Tuttle and Kockelman (2012)).

It uses an electric motor and an internal combustion engine (ICE), either in series or parallel

architecture (Electrification Coalition (2013)). In a series configuration, the ICE is only used

to power a generator and the electric motor is the only propulsion component coupled to

the final drive shaft, while a parallel configuration allows for both the ICE and the electric

motor to be coupled to the final drive shaft and to be used simultaneously or individually

(Chan (2007)). While the former offers a simpler design, the latter is more efficient due to

fewer energy conversions and to the non-essential sizing of components for maximum power

demands (Chan (2007)). A series PHEV is also often referred to as an Extended Range

Electric Vehicle (Tuttle and Kockelman (2012)). Parallel PHEVs only allow an electric range

in the lines of 30-60 km with lithium-ion batteries (Chan (2007)). PHEVs could be a viable

transition technology as it allows short trips to be made in electric mode and alternative

fuels to be used for longer itineraries (Abdallah (2013)).

2.1.3 Fuel cell electric vehicles

In an FCEV, a fuel cell generates electricity from hydrogen’s chemical energy, from which

the output is water, which then either powers the electric motor or charges a battery (Chan

(2007)). A battery can be used to store the electric motor’s regenerative breaking energy and

to assist the fuel cell during sudden load variations that it cannot handle by itself (Emadi

et al. (2005)). The hydrogen must be stored on-board either in gaseous or liquid states, or

through physical or chemical adsorption (den Boer et al. (2013)). FCEVs can be refueled

in a few minutes (Eberle and von Helmolt (2010)) and also offer quiet operation due to the

small number of their moving parts (Emadi et al. (2005)), but are less efficient than BEVs

since they must convert the hydrogen’s energy into electricity before powering the electric

motor (den Boer et al. (2013)). Fuel cells have efficiencies of approximately 50% in terms

of the proportion of the hydrogen’s energy being converted to electricity (US DOE (2013a))

and could also be an option for auxiliary power units in heavy-duty vehicles (Emadi et al.

(2005)). The cost of FCEVs is still an important market barrier for this technology (Mock

and Yang (2014)), so is the fuel cell’s durability which is currently 10,000 operating hours

at best (den Boer et al. (2013)).

2.2 Batteries

Batteries are a critical factor in the widespread adoption of electric vehicles since they have

a much lower energy density than gasoline (Hannisdahl et al. (2013)). The main options for


4 CIRRELT-2014-44

batteries in BEVs include lead acid batteries, nickel metal hydride batteries, and lithium-

ion batteries (Chan (2007)). Since lithium-ion batteries, compared to other options, have a

high energy density (100 Wh/kg), high power density (300W/kg), long battery life and low

memory effect (Lukic et al. (2008)), they are the most commonly used alternative for modern

passenger and freight BEVs (den Boer et al. (2013)). Energy densities could potentially be

tripled by 2030 with the development of technologies such as lithium-sulfur batteries (Duleep

et al. (2011)). The battery pack is also the most costly component in plug-in electric vehicles

and significantly increases the vehicle costs (Electrification Coalition (2013)). However,

significant reductions in battery costs are also expected in the next decades, with a potential

reduction by a factor of up to 10 by 2030, compared with 2009 (US DOE (2010)).

2.2.1 Battery lifespan

Batteries are considered to be no longer suitable for electric vehicles when their capacity has

been reduced to 80% of the original value (McMorrin et al. (2012)). Their health can be

influenced by the way they are charged and discharged. For example, battery deterioration

can occur if it is frequently discharged to deep levels (Millner (2010)). This generally implies

that only 80% of the marketed battery capacity should actually be used (Valenta (2013)).

Deep cycle life refers to the number of times a battery can be discharged to a low state

of charge, while shallow cycle life is the number of times it can withstand a small state

of charge variation (Duleep et al. (2011)). Lithium-ion batteries in currently used freight

BEVs typically provide a 1,000 to 2,000 deep cycle life, which should last around six years;

a 4,000 to 5,000 deep cycle life is expected within five years (den Boer et al. (2013)). Many

manufacturers have reached 200,000 cycles for shallow cycle life (Duleep et al. (2011)).

Frequently charging the battery too close to maximum capacity can also adversely affect

its lifespan (Sweda et al. (2014)), as can keeping it at high states of charge for long periods

(Electrification Coalition (2010)). Using high power levels to quickly charge batteries could

also have negative impacts, especially if used in the beginning and end of the charging

cycle (Zhang (2006)). However, there is still much uncertainty regarding the effects of using

high power levels (Hatton et al. (2009)), and recent testings suggest these may not be that

significant (Idaho National Laboratory (2014)). Nevertheless, all these factors can speed

up the reduction of usable battery capacity, hence shortening vehicle range and battery life

(Electrification Coalition (2010)). One way to prolong battery life and reduce their size

could be to use them alongside ultracapacitators, which have much higher power densities

(Khaligh and Li (2010)).


CIRRELT-2014-44 5

2.2.2 Battery charging

The most common way of charging batteries is conductive charging, thus implying the need

of a cable and a vehicle connector (Yılmaz and Krein (2013). Charging modes are defined by

the safety communication protocol between the vehicle and the charging equipment, while

charging types refer to the connector used. Overviews of modes and connector standards

can be found in Cluzel et al. (2013) and Naberezhnykh et al. (2012b). Charging levels can

be divided according to the power rate used, and different classifications are used according

to nationally available power levels (Haghbin et al. (2010)). Yılmaz and Krein (2013) refers

to three levels based on the SAE Standard J1772: level 1 (1.4 kW to 1.9 kW), level 2 (4kW

to 19.2 kW) and level 3 (50 kW to 100 kW), also referred to as fast charging.

Conductive chargers can either be on-board or off-board the vehicle, with unidirectional or

bidirectional power flow (Yılmaz and Krein (2013)). Using a higher power level requires larger

chargers, thus constraining on-board chargers to lower power levels because of weight, space

and cost (Haghbin et al. (2010)). Integrating the charger into the electric drive train is an

option to allow on-board high power charging (Haghbin et al. (2010)). While level 1 charging

can be supplied with a convenience outlet, levels 2 and 3 require dedicated electric vehicle

supply equipment (EVSE) and are the viable options in public charging infrastructures

(Yılmaz and Krein (2013)).

Charging times depend on the size of the batteries and the equipment used (US DOE

(2012b)). They vary from as little as 30 minutes to several hours, and are not linear during

the entire process: “the first phase allows to recharge the battery almost fully and it is linear

with respect to time. The second phase is not linear with respect to time and can require

some hours to achieve the full charge of the battery and to ensure a uniform recharge of all

the cells that compose the battery”(Bruglieri et al. (2014, p.20)).

In most cases where BEVs are used for goods distribution, they are mainly charged at

depots overnight and do not use public charging stations other than during drivers’ lunch

breaks (Nesterova et al. (2013), Naberezhnykh et al. (2012b), Taefi et al. (2014)). This can

be explained by the need to make cost effective use of driver time and by security concerns

(Naberezhnykh et al. (2012b)). Also, charging during off-peak hours can allow for reduced

electricity rates (Mock and Yang (2014)), and fast charging infrastructures necessary for

charging during delivery routes are insignificant in most countries (IEA and EVI (2013)).

Fast charging stations could be installed at the depot, but the high cost associated to level

3 EVSE limits interest in fast charging (Hatton et al. (2009)), and the impact of the high

power demand from the grid is another issue (Dharmakeerthi et al. (2014)). This could


6 CIRRELT-2014-44

limit the number of BEVs that could be simultaneously charged with high power levels

(Etezadi-Amoli et al. (2010)), potentially requiring investments for transformer upgrades

(Electrification Coalition (2010)).

While conductive charging is the most common method for charging electric vehicles,

there exist a few other options, such as inductive charging and battery swapping. Another

option for powering larger electric vehicles is catenary wires.

Inductive charging involves transferring the power to the battery magnetically via an on-

board charger, thus eliminating the need for cables and chords (Yılmaz and Krein (2013)).

Stationary inductive charging would be used to charge the battery while the vehicle is

stopped, such as on garage floors, in parking lots or at bus stops, while in-road inductive

charging could allow for charging the battery or powering the motor even while the vehicle

is in motion (den Boer et al. (2013)). Electric buses could be ideal candidates for both types

of inductive charging, but the power necessary for several trucks traveling close to each other

could limit applications for in-road charging in freight transportation (CALSTART (2013a)).

Battery swapping involves vehicles using automated battery swapping stations which

remove the depleted battery and insert a fully charged one (CALSTART (2013b)). Technical

barriers include the space and cost associated with the large stock of batteries needed, huge

infrastructure costs for the swapping stations, the necessary standardisation of vehicles and

batteries, and the risk of battery damage from excessive swapping (Mak et al. (2013), Hatton

et al. (2009), CALSTART (2013b), den Boer et al. (2013), Hazeldine et al. (2009)). The

stations would also need to recharge many batteries at the same time, which could impact

the electric grid (Mak et al. (2013)).

Using catenary wires as a power supply would only be a viable option on highly used free-

ways. Under this power option, overhead wires are charged with electricity and a truck uses

a pantograph device to slide along the wires and retrieve the power (CALSTART (2013b)).

The energy used for electric drive is not stored on-board, therefore allowing additional pay-

load capacity and unlimited electric range when connected to the wires (den Boer et al.

(2013)). However, infrastructure costs, business models for access, and visual pollution

constitute important barriers for a wider adoption in freight transportation (CALSTART

(2013b)).


CIRRELT-2014-44 7

3. Market penetration

Global manufacturers began producing passenger BEVs and PHEVs in 2010 (Tuttle and

Kockelman (2012)), but smaller manufacturers had been producing electric vehicles in several

classes much earlier (Sierzchula et al. (2012)), some of which have been used in the logistics

sector (Element Energy. (2012)). However, relatively few have produced heavy duty vehicles.

Also, in several cases, models on the market are conversions of conventional vehicles where

the engine has been replaced by the necessary electric components (US DOE (2012b)).

3.1 Market Shares

Global sales of passenger BEVs and PHEVs were approximately 10,000 in 2009, 45,000 in

2011, 110,000 in 2012, and 210,000 in 2013, with only a few national markets exceeding a 1%

market share of all passenger vehicles in 2013 (Mock and Yang (2014)). According to Berman

and Gartner (2013) of Navigant, approximately 37,000 BEVs and PHEVs were expected to

be sold for fleet purposes in 2013. Some of these vehicles are used in the delivery of lighter

goods. For example, battery electric cars have been tested in pizza delivery operations in

Hamburg (TU Delft et al. (2013)).

According to Jerram and Gartner (2013) of Navigant, the market for plug-in electric

trucks and vans has not taken off as well as that for passenger vehicles. These vehicles

have generally only penetrated niche applications while remaining dependent on government

incentives. This is partly due to key industry players going out of business, to the conservative

nature of fleet operators when it comes to new technologies, to a renewed interest in natural

gas, and to the substantial cost premium of EVs. Navigant’s reports executive summary

also states that the global stock of HEV, PHEV and BEV commercial trucks (class 2 to 8)

was around 20,000 at the time of publication, the vast majority of which were HEVs (which

do not require plug-in recharging).

Den Boer et al. (2013) state that approximatively 1,000 battery electric distribution

trucks were operated around the world as of July 2013. According to Parish and Pitkanen

(2012), industry experts have estimated there are fewer than 500 battery electric trucks in

use in North America as of 2012, most sales being made in US states with incentives for

these vehicles, such as California and New York. Also, approximately 4,500 hybrid trucks

were sold in North America as of 2012. The large majority of hybrid and battery electric

trucks sold were in medium duty and vocational applications rather than long-haul class 8

applications.


8 CIRRELT-2014-44

According to Electrification Coalition (2013), the lack of vehicle availability by OEMs

in heavier plug-in electric vehicles is another reason for their limited market penetration.

Most of these vehicles are offered by small start-up firms. The report states that even if fleet

operators tend to focus more on total cost of ownership than purchase costs, they also tend

to be risk averse. Therefore, new small manufacturers offering electric trucks have difficulty

in penetrating the market.

One of European project FREVUE’s reports (Nesterova et al. (2013)) lists similar factors

for the limited use of electric freight vehicles in city logistics, notably doubts on technology

readiness, high purchase costs, and the limited amount of models on the market. Issues with

accessing fast maintenance services from vehicle manufacturers can be another factor of doubt

in adopting these vehicles. The report also states that the rapid technology improvements

themselves can be a market barrier since a BEV purchased today could quickly lose its

residual value and result in missing out on a better generation of vehicles.

However, the Electrification Coalition (2010) has identified several factors making EVs at-

tractive for commercial applications, notably ther lower operational and maintenance costs

combined with high utilization rates of fleet vehicles, easy charge scheduling, route pre-

dictability, centralized depot recharging, commercial and industrial electricity rates, and

corporate image. Even so, a wider adoption of BEVs in distribution applications can only

be achieved if these prove to be cost-effective when compared to using conventional ICEVs

for the same application.

3.2 Cost competitiveness

Because of the different cost structures between ICEVs and BEVs, the only way to appro-

priately compare their cost competitiveness is to study their whole life costs (McMorrin

et al. (2012)). While commercial BEVs operational costs can be nearly four times lower

than diesel trucks, the downside is that their purchase costs are approximatively three times

higher (Feng and Figliozzi (2013)). Maintenance costs for BEVs have been reported to be

20% to 30% lower than those of ICEVs (Taefi et al. (2014)) and this advantage should in-

crease with the ICEVs’ age (Electrification Coalition (2010)). Combined to lower energy

costs, this makes electric vehicles more interesting for long planning horizons.

One way to decrease the cost premium and allow for additional payload capacity is to be

able to right-size the battery (Electrification Coalition (2013)). However, the smaller battery

would require more frequent deep discharges, which could accelerate battery deterioration

((Pitkanen and Amburg (2012)). Another option for reducing upfront costs while also ad-


CIRRELT-2014-44 9

dressing concerns about battery life is to lease the battery for a monthly fee based on energy

consumed or distance traveled (McMorrin et al. (2012)). However, uncertainties regarding

battery residual value limit many fleets’ interest in battery leasing (Pitkanen and Amburg

(2012)), most likely because these will be integrated into the leasing fee. While numerous

studies have analyzed whole life costs for battery electric cars (e.g., Tuttle and Kockelman

(2012), Prud’homme and Koning (2012), Thiel et al. (2010), Delucchi and Lipman (2001),

Offer et al. (2010)), the business case of commercial BEVs has been less treated.

Davis and Figliozzi (2013) have compared whole life costs of battery electric delivery

trucks with that of a conventional ICEVs serving less-than-truckload delivery routes. They

considered two types of BEVs and one type of ICEV. They simulated 243 different route

instances, varied by using different values for five parameters: the number of customers, the

service area, the depot-service area distance, the customer service time, and the customer

demand weight. Different battery replacement and cost scenarios were also studied. The

planning horizon was set to 10 years and the vehicles’ residual value at 20% of their purchase

price. The results show that although the BEVs’ TCO was highest in 210 out the 243

instances, a combination of factors can allow them to be a viable alternative: high utilization

rates, low speeds and congestion, frequent customer stops during which a ICEV would idle,

other factors amplifying the BEVs’ superior efficiency, financial incentives or technological

breakthroughs to reduce purchase costs, and a planning horizon above ten years

Feng and Figliozzi (2013) developed a fleet replacement optimization model for fleets with

one BEV type and one ICEV type. The objective is to minimize the discounted sum of all

costs over a planning horizon of 30 years. Several scenarios with varying annual utilization

rates and ICEV fuel efficiency were analysed. Findings indicated that the BEV becomes

competitive when used over 16,000 miles per year, especially in environments where the

ICEV has low fuel efficiency. The breakeven analysis indicated a planning horizon of at

least 12 years is necessary to recuperate high capital costs, and that in environments where

the ICEV fuel efficiency is high, a price reduction ranging from 9% to 27% for the BEV,

depending on utilization, would be necessary to achieve cost-effectiveness. The break even

values for the BEVs to be competitive were found to be within realistic value ranges for

congested urban areas. The sensitivity analysis identified the planning horizon, the purchase

price and the utilization level as critical factors for the BEV’s competitiveness. Including

a battery replacement in different scenarios was found to significantly decrease the electric

vehicles’ competitiveness.

The appropriateness of BEVs for goods distribution ultimately depends on the context of

their intended use, but the high purchase costs resulting from the batteries has been shown


10 CIRRELT-2014-44

to be a major cost effectiveness barrier. As a result, at this stage incentives are likely to be

needed to increase the commercial use of BEVs (Taefi et al. (2014)).

3.3 Incentives

Several national governments have already set up measures aimed at increasing the com-

mercial use of BEVs. Local policies can also be very useful for promoting BEVs for goods

distribution. A few interesting examples of such incentives are provided below.

3.3.1 Financial incentives

Financial incentives reduce the upfront costs of EVs and their charging equipment (IEA and EVI

(2013)). One form is purchase subsidies granted upon buying the vehicle (Mock and Yang

(2014)). Examples of this are the California Hybrid Truck and Bus Voucher Incentive Project

and the New York Truck Voucher Incentive Program which provide up to $50,000 and

$60,000 respectively towards battery electric truck purchases (Parish and Pitkanen (2012),

New York State Energy Research and Development Authority (2014)). In Europe, the city

of Amsterdam covers up to e40,000 for the purchase of such trucks (den Boer et al. (2013)).

Another example is the UK Plug-in Van Grant, which covers 20% of the cost of battery elec-

tric vans up to £8,000 (McMorrin et al. (2012)). Companies can also receive such subsidies

for participating in demonstration or performance evaluation projects (US DOE (2013b)).

In Germany, up to 50% of the investment can be covered by participating in such projects

(Taefi et al. (2014)). Furthermore, some countries such as Norway, the Netherlands, the

UK and France offer subsidies for the installation of the charging equipment (AustriaTech

(2014)). Finally, tax exemptions are another financial incentive used for lowering ownership

costs of EVs. These include exemptions from VAT, vehicle registration taxes, fuel consump-

tion taxes, and company car taxes (AustriaTech (2014)). Overviews of EV tax exemptions

can be found in IEA and EVI (2013), ACEA (2014), US DOE (2012a), and Mock and Yang

(2014).

3.3.2 Prioritized access incentives

Local incentives for prioritized access often yield significant daily cost and time savings

(Cluzel et al. (2013)). One mechanism is to grant BEVs access to high occupancy lanes or bus

lanes, such as in Utrecht, Lisbon, and Trondheim (Nesterova et al. (2013)). London and Oslo

have also experimented with such access incentives and have identified issues such as conflicts


CIRRELT-2014-44 11

with buses and insufficient coverage of the road network by the bus lanes (AustriaTech

(2014)). BEVs can also be exempted from certain road tolls, as is the case in Norway

(Hannisdahl et al. (2013)). Another concept used is Low Emission Zones (LEZs) in city

centres to reduce emissions and promote the use of cleaner vehicles. Many LEZs in which

electric vehicles are permitted can be found in Dutch cities. These apply to commercial

vehicles over 3.5 tonnes GVW (AustriaTech (2014)). In Rome, diesel freight vehicles pay an

annual fee of e570 to have access to the city centre, while electric freight vehicles pay e300

(AustriaTech (2014)). Another example is the London Congestion Charge; the daily rate for

accessing central London is £10, but electric freight vehicles are fully exempted from this

charge (McMorrin et al. (2012)). Another form of priority access which can be offered to

BEVs because of their noiseless operation is extended delivery time windows (Nesterova et al.

(2013)). For example, in s-Hertogenbosch, only silent trucks can enter the city centre between

12am and 7am , and similar testings have taken place in Barcelona and Dublin where the

extended time window was 10pm to 7am (AustriaTech (2014)). EVs can also be exempted

from restrictions regarding the maximum weight of vehicles allowed in city centres, as is the

case in Amsterdam (TU Delft et al. (2013)). Finally, preferential parking is another way to

encourage the use of commercial BEVs, either in allocating free spaces or designated loading

and unloading docks (AustriaTech (2014)). For example, in Bremen, an environmental

loading point close to the city centre provides a dedicated loading area exclusively for Euro 5

diesel vehicles and electric vehicles up to 7500 kg GVW (MDS Transmodal Limited (2012)).

4. Electric vehicles in transportation science

From an operational research point of view EVs and ICEVs share several features, namely

a restricted autonomy and a limited capacity. However, the autonomy of EVs is typically

more limited, the availability of recharging stations for EVs is rather restricted and their

recharging time is considerably longer, compared to that of ICEVs. Moreover, EC possess a

number of characteristics that differentiate them from ICEVs: they can sometimes recharge

at customer locations, possibly while service is being carried out, they can generate energy

by decelerating and battery efficiency depends on recharging policies. Finally, relations and

incentives may have an impact on the adoption of EVs. In what follows we will review the

main transportation science literature on EVs regarding fleet size and mix, vehicle routing,

and optimal paths.


12 CIRRELT-2014-44

4.1 Fleet size and mix

The fleet size and mix literature related to EVs is relatively recent and limited. It does not

yet cover all EV specific features just mentioned. We are aware of four recent contributions

in this area.

Van Duin et al.(2013) sought to determine the ideal fleet of BEVs for cargo distribution

to a set of customers in Amsterdam’s inner-city from a potential depot serving as a small

transhipment facility for deliveries in city centre. The problem is formulated as the Electric

Vehicle Fleet Size and Mix Vehicle Routing Problem with Time Windows. The aim is to

determine an optimal fleet of BEVs and delivery routes to offer a desired service level at

minimal costs to a set of customers with delivery time windows. Customer time windows,

service times, number of served customers and their demands are considered, as well as

the limited autonomy, reduced payloads, high purchase costs and low operational costs of

the BEVs. The model ensures that the vehicles always have enough energy to return to

the depot and to perform their tours without recharging, and that tours do not last longer

than a specified duration. The authors used a sequential insertion heuristic to solve a real

instance with two types of electric trucks: the Cargohopper Type 2.1 and the Cargohopper

Type 2.2. The number of customers varies according to the day of the week, ranging from

155 to 219. The fleet configuration with the lowest average cost per delivery and service level

over 99% was chosen among ten predetermined potential configurations and the best one was

recommended for implementation. They also determined scenarios in which the total cost

of operating these vehicles is lower than that of the current method of delivery, excluding

the consolidation centre’s operating costs. Their findings show the ability of modern BEVS

to perform urban distribution as well as to reduce air and noise pollution in city centres.

However, subsidies and a high utilization would be required for financial profitability.

Goncalves et al. (2011) have studied the VRP with Pickup and Delivery with a hetero-

geneous fleet of BEVs and ICEVs vehicles. The problem was formulated as a mixed integer

linear program in which the objective is to minimize total fixed and variable costs. Tour

duration and vehicle capacity constraints are considered in the model. Charging times are

fixed and depend on vehicle range. The vehicles can recharge anywhere during the routes,

and the only effect this has on the routes is a time penalty. The number of times the ve-

hicle must stop to charge is determined by dividing the total distance to be traveled by

the vehicle range. The model was applied to a Portuguese battery distributor which both

delivers batteries and recuperates them from its customers. Customers are also visited for

other reasons, such as technical assistance and collection of claims. Three scenarios were


CIRRELT-2014-44 13

evaluated: one using a fleet of two types of ICEVs, one using a fleet of two types of ICEVs

and one type of uncapacitated BEVs, visiting customers without pickups or deliveries, and

one using exclusively one type of BEVs. The results showed that using EVs is a more costly

alternative due to the high investment required for acquiring or converting these vehicles.

Bae et al. (2011) cast the EV and ICEV fleet size and mix problem as a two-player two-

stage game between a regulatory agency and an operator. The regulatory agency imposes

environmental standards and can offer tax and price incentives to encourage the acquisition

of EVs. It may also specify maximum emission constrains. The fleet operator works in a

completive environment and seeks to offer good quality service while minimizing its costs.

The authors developed and solved an equilibrium model for this game. They concluded that

while subsidies may increase the operator’s profit they may also result in higher prices for

the customers. However, these customers will benefit from a reduction in pollution if more

EVs are used. In addition, energy price shocks and the resulting intensification in price

competition will put late EV adopters at a disadvantage, but as the benefits of EVs become

clearer, their adoption should increase, leading to more price competition and benefits to the

customers. While imposing stricter environmental regulations should increase the adoption of

EVs, providing subsidies may induce better results. Finally, a wider initial quality difference

in products and services between firms is likely to result in a greater response from latecomers,

resulting again in higher competition.

As discussed previously, Feng and Figliozzi (2013) have also studied the BEV and ICEV

fleet size and mix problem; readers may refer to Section 3.2 for more information on their

findings.

4.2 Vehicle routing

In recent years there has a growing interest in the integration of environmental aspects in

the study of the Vehicle Routing Problem (VRP) and its variants. For recent surveys, see

Demir et al. (2014) and Lin et al. (2014). In what follows we survey some recent contribution

to this area in the context of EVs.

One interesting problem is the Green VRP (GVRP) introduced by Erdogan and Miller-

Hooks (2012). In this problem, vehicle routes and refueling (or recharging) of alternative-fuel

powered vehicles (AFVs) at alternative fuel stations (AFSs) are determined simultaneously.

The authors formulated the problem as a mixed integer linear program whose objective is

the minimization of the distance traveled considering a limited driving autonomy and the

given locations of of AFSs, as well as constraints on the number of tours and their dura-


14 CIRRELT-2014-44

tion. However, vehicles are assumed to be uncapacitated. Refueling time is assumed to be

constant, an assumption which may not be ideal for using the model for BEVs unless a fast

charging infrastructure is available. Customer service time is also assumed to be constant.

The authors used a modified Clarke and Wright savings heuristic and a density-based clus-

tering algorithm to construct a set of feasible tours, followed by a postoptimization phase.

Numerical experiments were performed using typical parameters for biodiesel-powered vehi-

cles. Forty small problem instances with 20 customers were designed to evaluate the impacts

of customer configuration and the number of AFSs on the heuristics performance compared

to exact solutions obtained via CPLEX. Twelve larger problem instances with up to 500

customers inspired from a medical textile supply company in Virginia were also tested. The

results indicate that as the number of AFSs increases, costs decrease for the same number of

served customers, more customers can be served, and the total distance traveled decreases.

A few other papers have also studied the GVRP, either by directly focusing on BEVs or

by considering alternative fuel vehicles in general.

Taha et al. (2014) have also proposed a mixed integer linear programming formulation for

the GVRP, different from that of Erdogan and Miller-Hooks (2012) by allowing return paths

visiting more than one AFS. They solved the small sized instances proposed by Erdogan and

Miller-Hooks (2012) to optimality.

Omidvar and Tavakkoli-Moghaddam (2012) have added delivery time windows, customer

demands and vehicle capacity constraints to the GVRP, while also integrating the effect of

congestion. The working day is discretized into intervals with different congestion levels,

speeds and travel times. The AFVs can refuel at AFSs along their routes and can stay at

customer locations during congestion periods. The objective is the minimization of the sum

of vehicle, travel and emissions cost. Small instances were solved to optimality, while larger

instances were solved by a simulated annealing (SA) and by a genetic algorithm (GA). The

SA yielded better results than the GA at the expense of larger computational times.

Schneider et al. (2014) also extended the GVRP by integrating time windows, customer

demands and capacity constraints to the problem, while focusing exclusively on BEVs, in

contrast to the previous studies focusing on all AFVs. As a result, recharging times were

dependent on the vehicles battery charge upon arrival at a recharging station, i.e., they were

not fixed. However, batteries must be fully recharged whenever charging occurs. The authors

considered a hierarchical objective function whose first term is the fleet size minimization and

whose second term is the minimization of the total traveled distance. They have developed a

hybrid metaheuristic combining variable neighborhood search (VNS) with tabu search (TS).

Their heuristic could solve the small size instances to optimality with up to 15 customers


CIRRELT-2014-44 15

and yielded an average gap of 0.35% on larger instances involving up to 100 customers and

21 recharging stations.

Montoya et al. (2014) also studied the GVRP, while focusing on general fleets of zero-

emission vehicles like Erdogan and Miller-Hooks (2012)), rather than exclusively on BEVs

like Schneider et al. (2014). Their objective is to minimize total distance traveled to serve a

set of customers considering vehicle range limitations, maximum route duration and available

AFS infrastructure. Service times and refueling times are assumed to be constant, and no

time windows are considered, as in Erdogan and Miller-Hooks (2012). The authors solved

the problem by means of a modified multi-space sampling heuristic which includes three

randomized Traveling Salesman Problem (TSP) heuristics within a route-first cluster-second

approach, as well as an autonomy reparation procedure to ensure route feasibility. They

compared their results to those obtained by Erdogan and Miller-Hooks (2012) and Schneider

et al. (2014) and found their approach to be competitive while being relatively simple.

Another variant of the VRP with BEVs studied by Conrad and Figliozzi (2011)) is

called the Recharging VRP (RVRP). However, in comparison to the previous VRP variants

where recharging was allowed en route at dedicated stations, here recharging occurs at the

customer locations. A service time penalty is incurred when a vehicle recharges, and this

can be done while servicing customers. Vehicle capacity constraints and customer time

windows are considered. Customers have identical service times but unique demands. The

objective function is hierarchical, the first term being to minimize the number of routes and

the second being to minimize the total costs associated with distance traveled, service time,

and recharging time penalties. The authors used a heuristic based on iterative construction

and an improvement algorithm for the capacitated VRP with time windows, described in

Figliozzi (2010). They conducted experiments on instances containing 40 customers. The

results show an increase in distance traveled and in the number of vehicles for lower vehicle

ranges and longer charging times, and hard time windows significantly increase fleet size and

distance traveled while also complicating recharging feasibility.

Another interesting variant to the VRP with EVs is to integrate PHEVs in the problem,

as was done by Abdallah (2013). The aim of this problem is to minimize the cost of using

a fleet of PHEVs to serve a set of customers, by minimizing the time these vehicles run on

gasoline while respecting customer time windows. In this case, minimizing total distance does

not guarantee minimal costs since the lower operational costs of the electric drive mode has

the potential of offering more savings even if longer routes are traveled, but in the electric

mode. The model imposes constraints on the number of vehicles available and on their

loading capacities. It assigns them to customers while respecting time windows, service


16 CIRRELT-2014-44

times and demands. At each node, there is the possibility to either charge the vehicles

battery to achieve a greater electric range, or to continue to the next open time window

using the ICE if the battery dies. An interesting feature of this problem is that one can

choose how much to charge the battery according to a charge function, rather than force a

full charge to maximum capacity as in the previous studies. This makes the model more

usable if fast charging is not available, since trade-offs can be made between the time spent

charging and the required energy for feasible routes. The author first solves the problem by

using Lagrangian relaxation to find solution lower bounds, and then apply tabu search.

Barco et al. (2012) study a public transportation VRP variant with BEVs while also

coordinating the charging schedule of the vehicles to minimize costs and battery degradation.

Customer node demands therefore specify passengers to be picked up and dropped off at

another node, rather than goods to be delivered. An energy consumption model is applied to

the VRP graph so that all edges between two nodes represent minimum consumption paths.

The objective function is to minimize the energy consumption of all routes while answering

all transport demands. Once this is done, the charging schedule problem minimizes the

charging and battery degradation costs in order to be able to perform the set of routes in the

VRP solution. Charging costs vary according to energy prices in different charging stations

and at different times of the day. Battery degradation costs result from factors like the

charging power level, the average state of charge and the depth of discharge. A differential

evolution algorithm is applied to solve the charging schedule problem, while the optimization

tool XPRESS was used to solve the VRP.

4.3 Optimal paths

Several studies focused on the case of a single electric vehicle traveling from an origin to a

destination by trying to determine an optimal path with respect to energy consumption, cost

or time. Others consider a predetermined path and attempt to find an optimal recharging

policy for minimizing total recharging costs. In what follows we present a few of these

studies.

Artmeier et al. (2010) consider the problem of energy-optimal routing of BEVs, which

is similar to the classical shortest path problem. However, the problem is different for

BEVs since these vehicles can recuperate energy while decelerating by using their electric

motor for regenerative breaking. The authors therefore allow certain edges of the graph to

have negative costs in terms of energy consumption, for example when the vehicle travels

downhill. Limitations due to the battery capacity mean that not all origin-destination paths


CIRRELT-2014-44 17

are feasible. Therefore, the problem is a constrained shortest path problem, in which the

battery charge is bounded by its capacity. The aim of the problem is to determine an origin-

destination path, given an initial battery charge, so that the constraints on the battery charge

are respected and the remaining charge upon arriving at the destination is highest. The

authors proposed algorithms with a worst-case cubic complexity for this problem. Numerical

experiments were performed on a road network near Munich containing 776,419 nodes and

1,713,900 edges. Eisner et al. (2011) and Sachenbacher et al. (2011) have later developed

more efficient algorithms for the same problem.

Fontana (2013) also studied the problem of determining optimal paths for BEVs and

incorporated some stochastic features. However, here the focus is not only on energy con-

sumption but also on travel time. The objective is to determine an origin destination path

of least duration as well as the vehicle speed, while respecting an energy consumption limit

and considering uncertainties regarding time and energy consumption resulting from traf-

fic, weather, accidents, other drivers’ behavior, etc. The author has developed an energy

consumption model to determine the expected consumption on each edge. Energy recu-

peration on certain edges is considered. Several solution methods were proposed including

Lagrangian relaxation, techniques from robust optimization, A* search, and dynamic pro-

gramming. Real-world data were used to construct instances based on typical trips in Mas-

sachusetts and Michigan, on which numerical experiments were conducted. Instances were

varied for same trips by setting different energy consumption requirements. Estimates for

lower bounds obtained by Lagrangian relaxation suggest that the proposed algorithms could

find near-optimal solutions within a few seconds. Trade-offs between several paths duration

and energy consumption were also identified using a Pareto frontier, indicating that changing

routes can often significantly reduce energy consumption while only slightly increasing travel

time.

Sweda and Klabjan (2012) considered the problem of determining a minimum-cost path

for electric vehicles and integrated the idea of allowing the battery to be recharged along

the path. The aim is to determine a minimum cost origin destination path, such that the

battery charge always remains above a minimum threshold and below its capacity. In this

case, the authors do not use zero as the lower bound on the battery charge constraint because

of potential battery degradation resulting from very low depths of discharge. The problem

is formulated as a dynamic program. The terms used in the optimality equations refer to

the cost of recharging, the cost of traveling between two nodes, and the value function upon

reaching the next node. A backward recursion algorithm is applied to find an optimal path.

However, certain conditions must be satisfied in order for the state-space to be discrete and


18 CIRRELT-2014-44

the backward recursion algorithm to be applicable. Therefore, the authors also propose an

approximate dynamic programming algorithm for more general instances.

Sweda et al. (2014) studied the problem of determining optimal charging policies along a

predetermined path which are cost effective and improve battery longevity. The objective is

to minimize the total cost of recharging along the path according to the initial battery level at

the origin node, while constraining the battery charge to always remain above zero and below

maximum capacity. A stopping cost is used when the driver stops to charge the battery at a

node, considering factors such as the time taken to do so, fees charged by the station owner,

reducing battery life by one cycle, and a penalty if frequent stops are made. Overcharging

costs are incurred when charging the battery beyond a threshold over which degradation

occurs, and these costs increase when using higher voltages. The authors derive optimal

charging policies for general paths where charging is only allowed at prespecified nodes,

and for two specific path types:paths with continuous charging capability where charging

can be done anywhere along the path, and paths with equidistant charging locations at

prespecified nodes. An optimal policy is determined through a forward recursion algorithm

for the general paths, while the optimal number of stops for the continuous and equidistant

charging paths are obtained by solving a closed-form expression before determining the

corresponding optimal policy. The authors have also developed two heuristic methods to find

fast, simple and effective policies. They have tested the performance of their methods using

data of the US Interstate 90, and data generated from a simulated urban route with several

origin-destination trips and different values for the driving range, overcharging threshold,

and overcharging cost rates. The optimal policies show that overcharging is more costly

than recharging more frequently when considering degradation costs.

Arslan et al. (2014) have integrated PHEVs into the minimum cost path between two

points. They consider battery swapping stations instead of battery charging stations. In

this context, the option of refuelling the vehicle with gasoline is also available. Since a short-

est path is not necessarily the minimum cost path in this context because the operational

costs are lower for electric driving, a depreciation cost is used to penalize long distances.

Additionally, a cost is incurred for each stop . Battery degradation costs are associated with

deep battery discharges. The objective is to minimize the total travel cost associated with

electricity and gasoline, the battery degradation cost, the depreciation cost and the stopping

cost. The authors propose three solution techniques: a mixed integer quadratic constrained

program, a dynamic programming based heuristic, and a shortest path heuristic. Numerical

experiments were conducted using problem instances that represent various network struc-

tures and user behaviors. CPLEX solves the problem to optimality, but running times can


CIRRELT-2014-44 19

become quite high as the node number increases. The heuristic methods can solve much

larger problem instances and find near-optimal solutions within reasonable running times.

Kobayashi et al. (2011) also seek to determine an optimal origin-destination path for

BEVs while allowing the vehicles to stop at charging stations if necessary. Slow and fast

recharging stations are considered, but regenerative breaking is not taken into account in

their approach. The methodology first identifies potential stations when the vehicle cannot

complete the path from its origin with its remaining charge. Dijkstra’s algorithm is then

used to find a least travel cost route using the identified stations. Depending on the driver’s

priority, traveling costs either focus on distance traveled or on traveling and charging time.

The charging level of the stations, in addition to their location, is considered. However,

no battery degradation component is considered as a result of excessive fast charging, and

the vehicles must be fully charged when stopping at a station. Numerical experiments were

conducted on a Japanese map with 2,500 hypothetical charging stations (2,200 slow stations

and 300 fast stations), while prioritizing travel time rather than distance. Results showed

that the method is effective but also requires more computational time than conventional

route search methods due to the calculations needed for finding potential stations.

Alesiani and Maslekar (2014) are also interested in determining optimal paths for BEVs

by allowing them to stop at charging stations along the way if necessary. A feature of this

problem is that individual paths are determined jointly for a fleet of BEVs, each vehicle

having its own origin-destination pair. Furthermore, the authors incorporate the idea of

allocating charging stations to the vehicles so as to avoid having many vehicles at the same

one simultaneously. The goal is to minimize total costs related to energy consumption and

charging times resulting from concurrency at the stations, while respecting constraints with

regards to battery capacity, the number of charging stops allowed per route, and the number

of vehicles that can be charged at the stations. Energy recuperation is also considered. The

problem is solved with a modified evolutionary genetic algorithm. Numerical experiments

show that this approach allows a better distribution of the vehicles among the stations.

Siddiqi et al. (2011b) and Siddiqi et al. (2011a) exclusively consider fast charging stations

along the path and use a multi-constrained optimal path (MCOP) algorithm to determine

an optimal origin-destination route. The objective is to minimize total distance traveled

while respecting constraints regarding travel times, traffic delays, charging times and charg-

ing costs. Charging times also include waiting times that can occur at the stations. The

MCOP formulation is first transformed into an unconstrained optimization problem by using

a penalty function method, which is then solved with an algorithm based on simulated evo-

lution in (Siddiqi et al. (2011b)) and particle swarm optimization in (Siddiqi et al. (2011a)).


20 CIRRELT-2014-44

The algorithms were implemented in Java, and numerical experiments based on a Saudi Ara-

bian city with 108 nodes and 432 edges show the superiority of their method compared to

other popular MCOP algorithms. Siddiqi et al. (2012) further studied the problem by using

a simulated evolution based algorithm but on a multi-objective shortest path formulation of

the problem. Another distinction is that the stations are placed at road intersections (i.e.,

nodes) rather than along road segments (i.e., edges) as in their MCOP approach. The goal

is to minimize three objective functions regarding charging time, path length and travel time

respectively, while never running out of energy. Numerical experiments were carried out on

the San Francisco Bay Area and on Colorado road networks.

5. Research perspectives

Compared to ICEVs, EVs generally pollute less and produce less noise and are thus perceived

as a more sustainable means of goods transportation. As noted in Section 3.3, the societal

advantages of using EVs translates into a number of incentive schemes designed to increase

the competitiveness of EVs.

From a cost perspective, EVs require lower maintenance costs and incur lower operat-

ing costs, but have high purchasing costs. As mentioned by Feng and Figliozzi (2013), the

purchase cost of commercial electric vehicles can be three times higher than that of ICEVs,

while the operational costs of commercial electric vehicles can be nearly four times lower.

This, however, depends on the price of electricity in the market of operation. At present,

the adoption of EVs for goods transportation is somewhat dependent on incentives, thus

indicating that this greener form of transportation is not yet economically viable. There-

fore, the cost competitiveness of EVs highly depends on future technological developments

related to these vehicles, coupled with governmental investment in adequate infrastructure

and incentives. Nonetheless, a number of important features can already be identified as

necessary when studying freight distribution with electric vehicles.

Based on the review presented in the previous sections, we will present a number of

important interrelated modelling issues that are relevant to the emerging research area of

goods distribution with EVs. In Sections 5.1–5.3, we discuss research perspectives at the

strategic, tactical and operational levels.


CIRRELT-2014-44 21

5.1 Strategic perspectives

The strategic perspectives of BEVs ownership in goods transportation mostly relate to fleet

composition issues, i.e., the best choice of fleet size and mix when considering a potential mix

of ICEVs and EVs. The ownership of BEVs should encompass purchase cost, operational

and maintenance costs.

A substantial part of the purchase cost of BEVs is related to the battery cost. The battery

size and weight influence the payload of BEVs, and thus impacts the effective capacity of

vehicles. The size of the battery also influences the driving range of BEV. Therefore, choosing

the correct battery type is paramount in fleet acquisition questions. We note that significant

reductions in battery costs and weights are also expected in the future. Thus, it is worth

examining the impact that such developments may have on fleet composition. Furthermore,

battery aging will gradually reduce the maximum range over time. Other factors influencing

the battery lifetimes are discussed in the following sections.

The use of BEVs and ICEVs essentially implies dealing with the potential distance ranges

that these vehicles can traverse without recharging or refuelling, respectively. The issue of

refuelling ICEVs has been rightfully ignored by the transportation science community since

there exists an abundance of fuel stations for conventional vehicles, the refuelling times

are negligible and refuelling stations offer similar services. However, these conditions do

not hold for BEVs. Indeed, the recharging infrastructure is scarce and refuelling times

can be heavily affected by the type of service offered by the particular recharging station.

Two main categories of charging stations exist: slow (level 2) and fast (level 3), the latter

being much more costly. Furthermore, fast charging dictates high power demand levels from

the electricity grid. Thus, the charging infrastructure depends on the local electricity grid

capabilities.

Naturally, charging capacities are subject to technological developments and should be

considered. However, the decision of purchasing BEVs should explicitly account for the ex-

isting infrastructure since this influences the strategic capacity of BEVs in terms of potential

range. Furthermore, a fleet operator may also consider investing in recharging infrastruc-

tures, at depots, customer locations or even along long-haul paths.

A measure for the battery life is the number of times a battery can be discharged to a low

state of charge. Lithium-ion batteries currently used for freight BEVs typically provide a

1,000 to 2,000 deep cycle life, which roughly translates into six years of operation (den Boer

et al. (2013)). This should be carefully considered in fleet investment decisions. Finally, the

fleet size and mix issues should not be limited to battery electric vehicles, but should also


22 CIRRELT-2014-44

include HEVs and FCEVs.

5.2 Tactical perspectives

Once purchased, BEVs necessitate taking a number of tactical decisions mainly pertaining

to charging policies, which in return influence the battery lifespan. While there exist several

ways of recharging batteries, the most common is conductive charging. Therefore, consider-

ing the current state of the conductive charging technology, one important decision relates

to where the EVs can recharge. This can be a combinations of charging at the depot, at a

customer location or at a charging station.

In reality, when BEVs are used for goods distribution, it is common not to use the public

infrastructure and to fully charge them overnight at the depot (Nesterova et al. (2013),

Naberezhnykh et al. (2012b), Taefi et al. (2014)). While charging at customers’ location is

an option, it requires a joint agreement with customers, with respect to charging times and

rates.

Recharging along a path, be it at a public station or at a customer location, would

make sense if fast charging could be performed at these stations. The slow recharging

infrastructure would require several hours to fully recharge a typical BEV, and therefore

would be less relevant considering a daily shift in short-haul transportation. Additionally,

fast charging infrastructures remain relatively scarce in most countries except Japan (IEA

and EVI (2013)). Therefore, for most countries it seems logical that vehicles should mostly

recharge at the depot. Furthermore, fast charging reduces battery lifespan. It particularly

affects battery health when used at the beginning or at the end of the charging cycle (Zhang

(2006)). The cost of recharging should also be accounted for when addressing the issue of

charging policies, since electricity is often cheaper during the night and off-peak hours.

Finally, a potential tactical decision relates to leasing batteries. Battery leasing is cur-

rently available for a few electric vans but not for electric trucks. The existence of such an

option for trucks might help their market penetration.

5.3 Operational perspectives

Considering the tactical recharging policy, operational decisions should be made in an at-

tempt to optimize the battery life while not compromising the operational cost of serving

customers. Charging batteries too close to their maximum capacity or keeping them at

high states of charge for long periods can adversely affect their lifespan (Sweda et al. (2014)


CIRRELT-2014-44 23

and Electrification Coalition (2010)). Furthermore, battery deterioration can also occur if

it is frequently discharged to deep levels (Millner (2010)). Therefore, deciding when and

how much to recharge is a critical operational issue since this directly impacts the potential

distances traversed by the BEVs.

Some models work with fixed charging times, or assume that the battery is charged to

its full capacity whenever it is charged. More realistic models could explicitly target battery

charge times and schedules as a means to optimize battery life. Furthermore, the BEV’s

payload should also be accounted for especially in light of its relationship with the battery

size.

The literature on optimal paths addresses battery properties in a more explicit fashion,

e.g., it considers the negative impacts of charging with higher power levels and too close to

maximum capacity, as well as discharging to low levels. Another example is to explicitly

account for regenerative braking along the paths. This leads us to the conclusion that

the elaborate battery health models should be gradually transferred to the vehicle routing

context.

The competitiveness of BEVs is amplified when considering congestion, which is partic-

ularly realistic in the case of urban distribution. Finally, a number of random factors may

temporarily reduce the BEV’s range, e.g., extreme temperature, high driving speeds, rapid

acceleration and driving up important slopes (US DOE (2012b)). These can be captured by

designing stochastic models in order to quantify their impact on the achievable range.

6. Conclusions

The integration of EVs in goods transportation depends on a number of factors related

to cost, technology, infrastructure, electricity sources and financial incentives. Since future

trends in these areas are highly uncertain, the problems needed to be solved by the trans-

portation science community are also likely to evolve over time, thus creating important

research challenges. However, we believe that modelling these features and developing ade-

quate solution procedures to the resulting models is vital as it allows the study of the current

problems and of future scenarios, within a rigorous framework.

Considering the current state of technology, BEVs are more appropriate for city distribu-

tion than long-haul applications because of the battery limitations. Typical daily distances

traveled by urban delivery trucks are often lower than the range of commercial BEVs (Feng

and Figliozzi (2013)), which has been shown to be suitable for real routing patterns of lo-


24 CIRRELT-2014-44

gistic service providers, even if the vehicles’ reduced payload remained a concern expressed

by drivers (Ehrler and Hebes (2012)). Therefore, BEVs are more suitable for last-mile de-

liveries in urban areas involving frequent stop-and-go movements, limited route lengths as

well as low travel speeds (Nesterova et al. (2013), TU Delft et al. (2013)). Their reduced

pollution and noise is also more relevant in urban areas and thus more likely to be rewarded

by incentives. These factors should certainly be of concern to researchers working in the

area of city logistics.

Finally, we highlight the fact that BEVs do not only compete against ICEVs, but also with

other cleaner alternatives such as compressed natural gas, in which the superiority of BEVs

can be even harder to establish (Valenta (2013)). Furthermore, significant improvements in

ICE vehicle efficiencies can be expected in upcoming years (Mosquet et al. (2011)). This

fact should not be ignored in the fleet size and mix research.

Acknowledgments

This research was partly supported by the Canadian Natural Sciences and Engineering Re-

search Council under grants 463834-2014, 436014-2013 and 39682-10. This support is grate-

fully acknowledged.

References

Abdallah, T. 2013. The plug-in hybrid electric vehicle routing problem with time windows. Mas-

ter’s thesis, University of Waterloo, Waterloo, Ontario, Canada. URL https://uwspace.

uwaterloo.ca/bitstream/handle/10012/7582/Abdallah_Tarek.pdf?sequence=1. Last ac-

cessed 19/5/2014.

ACEA. 2014. Overview of purchase and tax incentives for electric vehicles in the EU. URL http:

//www.acea.be/uploads/publications/Electric_vehicles_overview__2014.pdf. Last

accessed 15/5/2014.

AeroVironment. 2011. Fleet fast charging station, 250 kW DC. URL http://evsolutions.avinc.

com/uploads/products/5_AV_EV250-FS_061110_fleet_dc.pdf. Last accessed 7/8/2014.

Alesiani, F., N. Maslekar. 2014. Optimization of charging stops for fleet of electric vehicles: A

genetic approach. IEEE Intelligent Transportation Systems Magazine 6(3) 10–21.

Allen, J., G. Thorne, M. Browne. 2007. BESTUFS good practice guide on urban freight trans-

port. BESTUFS consortium. URL http://www.bestufs.net/download/BESTUFS_II/good_

practice/English_BESTUFS_Guide.pdf. Last accessed 14/6/2014.


CIRRELT-2014-44 25

Arslan, O., B. Yıldız, O.E. Karasan. 2014. Minimum cost path problem for plug-in hybrid electric

vehicles. Department of Industrial Engineering, Bilkent University, Turkey. URL http://

www.optimization-online.org/DB_FILE/2014/02/4230.pdf. Last accessed 23/7/2014.

Artmeier, A., J. Haselmayr, M. Leucker, M. Sachenbacher. 2010. The optimal rout-

ing problem in the context of battery-powered electric vehicles. Second International

Workshop on Constraint Reasoning and Optimization for Computational Sustainabil-

ity . Bologna, Italy. URL http://www.cis.cornell.edu/ics/compsust-org/crocs-at-

cpaior10/papers/crocs-at-cpaior10-Artmeier.pdf. Last accessed 7/7/2014.

AustriaTech. 2014. Electric fleets in urban logistics: Improving urban freight ef-

ficiency in small and medium-sized historic towns. Published as part of the

ENCLOSE project, funded by Intelligent Energy Europe (IEE), Vienna, Aus-

tria. URL http://www.austriatech.at/files/get/834747f18fdcc9538376c9314a4d7652/

austriatech_electricfleets_broschuere.pdf. Last accessed 22/5/2014.

Bae, S.H., J. Sarkis, C.S. Yoo. 2011. Greening transportation fleets: Insights from a two-stage

game theoretic model. Transportation Research Part E: Logistics and Transportation Review

47(6) 793–807.

Barco, J., A. Guerra, L. Muoz, N. Quijano. 2012. Optimal routing and scheduling of charge for

electric vehicles: Case study. Universidad de los Andes, Bogota, Colombia. URL http:

//arxiv.org/ftp/arxiv/papers/1310/1310.0145.pdf. Last accessed 12/6/2014.

Bektas, T., G. Laporte. 2011. The pollution-routing problem. Transportation Research Part B:

Methodological 45(8) 1232–1250.

Berman, B., J. Gartner. 2013. Report executive summary: Selecting electric vehicles for fleets.

Navigant Research. URL http://www.navigantresearch.com/wp-assets/uploads/2013/

02/RB-SEVF-13-Executive-Summary.pdf. Last accessed 5/6/2014.

Bruglieri, M., A. Colorni, A. Lue. 2014. The vehicle relocation problem for the one-way electric

vehicle sharing: An application to the Milan case. Procedia-Social and Behavioral Sciences

11 18–27.

CALSTART. 2013a. I-710 Project zero-emission truck commercialization study final report.

Pasadena, California. URL http://www.calstart.org/Libraries/I-710_Project/I-710_

Project_Zero-Emission_Truck_Commercialization_Study_Final_Report.sflb.ashx.

Last accessed 20/5/2014.

CALSTART. 2013b. Technologies, challenges and opportunities: I-710 Zero-emission freight

corridor vehicle systems (Revised Version Final V1). URL http://www.calstart.org/

Libraries/I-710_Project/Technologies_Challenges_and_Opportunities_I-710_Zero-

Emission_Freight_Corridor_Vehicle_Systems.sflb.ashx. Last accessed 2/6/2014.

Chan, C.C. 2007. The state of the art of electric, hybrid, and fuel cell vehicles. Proceedings of the

IEEE 95(4) 704–718.


26 CIRRELT-2014-44

Cluzel, C., B. Lane, E. Standen. 2013. Pathways to high penetration of electric ve-

hicles. Element Energy and Ecolane, commissioned by The Committee on Cli-

mate Change. URL http://www.theccc.org.uk/wp-content/uploads/2013/12/CCC-EV-

pathways_FINAL-REPORT_17-12-13-Final.pdf. Last accessed 10/6/2014.

Conrad, R.G., M.A. Figliozzi. 2011. The recharging vehicle routing problem. T. Doolen, eds

E. Van Aken, eds., Proceedings of the 2011 Industrial Engineering Research Conference.

URL http://web.cecs.pdx.edu/~maf/Conference_Proceedings/2011_The_Recharging_

Vehicle_Routing_Problem.pdf. Last accessed 16/6/2014.

Davis, B.A., M.A. Figliozzi. 2013. A methodology to evaluate the competitiveness of electric delivery

trucks. Transportation Research Part E: Logistics and Transportation Review 49(1) 8–23.

de Santiago, J., H. Bernhoff, B. Ekergard, S. Eriksson, S. Ferhatovic, R. Waters, M. Leijon. 2012.

Electrical motor drivelines in commercial all-electric vehicles: A review. IEEE Transactions

on Vehicular Technology 61(2) 475–484.

Delucchi, M.A., T.E. Lipman. 2001. An analysis of the retail and lifecycle cost of battery-powered

electric vehicles. Transportation Research Part D: Transport and Environment 6(6) 371–404.

Demir, E., T. Bektas, G. Laporte. 2014. A review of recent research on green road freight trans-

portation. European Journal of Operational Research 237(3) 775–793.

den Boer, E., S. Aarnink, F. Kleiner, J. Pagenkopf. 2013. Zero emission trucks: An overview

of state-of-the-art technologies and their potential. CE Delft and DLR, commissioned by

the International Council on Clean Transportation (ICCT). URL http://www.cedelft.eu/

publicatie/zero_emission_trucks/1399. Last accessed 23/5/2014.

Dharmakeerthi, C.H., N. Mithulananthan, T.K. Saha. 2014. Impact of electric vehicle fast charging

on power system voltage stability. International Journal of Electrical Power & Energy Systems

57 241–249.

Duleep, G., H. van Essen, B. Kampman, M M. Grunig. 2011. Impacts of electric vehicles - deliver-

able 2: Assessment of electric vehicle and battery technology. CE Delft, ICF International, and

Ecologic, commissioned by the European Commission. URL http://www.cedelft.eu/?go=

home.downloadPub&id=1153&file=4058_D2defreportHvE_1314726004.pdf. Last accessed

8/5/2014.

Eberle, U., R. von Helmolt. 2010. Sustainable transportation based on electric vehicle concepts: a

brief overview. Energy & Environmental Science 3(6) 689–699.

Ehrler, V., P. Hebes. 2012. Electromobility for city logistics - the solution to urban transport

collapse? An analysis beyond theory. Procedia-Social and Behavioral Sciences 48 786–795.

Eisner, J., S. Funke, S. Storandt. 2011. Optimal route planning for electric vehicles in large net-

works. 25th Association for the Advancement of Artificial Intelligence (AAAI) Conference.

San Francisco, California, 1108–1113. URL http://www.aaai.org/ocs/index.php/AAAI/

AAAI11/paper/viewFile/3637/4005. Last accessed 14/7/2014.


CIRRELT-2014-44 27

Electrification Coalition. 2010. Fleet electrification roadmap. URL http://www.

electrificationcoalition.org/sites/default/files/EC-Fleet-Roadmap-screen.pdf.


Electrification Coalition. 2013. State of the plug-in electric vehicle market. URL

http://www.electrificationcoalition.org/sites/default/files/EC_State_of_

PEV_Market_Final_1.pdf. Last accessed 5/5/2014.

Element Energy. 2012. State of the art - Commercial electric vehicles in western urban Europe.

Commissioned by the Cross River Partnership (CRP) within the URBACT II programme.

URL http://urbact.eu/fileadmin/Projects/EVUE/documents_media/OP_State_of_the_

Art_report_May_20121.pdf. Last accessed 28/5/2014.

Emadi, A., K. Rajashekara, S.S. Williamson, S.M. Lukic. 2005. Topological overview of hybrid elec-

tric and fuel cell vehicular power system architectures and configurations. IEEE Transactions

on Vehicular Technology 54(3) 763–770.

Erdogan, S., E. Miller-Hooks. 2012. A green vehicle routing problem. Transportation Research Part

E: Logistics and Transportation Review 48(1) 100–114.

Etezadi-Amoli, M., K. Choma, J. Stefani. 2010. Rapid-charge electric-vehicle stations. IEEE

Transactions on Power Delivery 25(3) 1883–1887.

Feng, W., M. Figliozzi. 2013. An economic and technological analysis of the key factors affecting

the competitiveness of electric commercial vehicles: A case study from the USA market.

Transportation Research Part C: Emerging Technologies 26 135–145.

Figliozzi, M.A. 2010. An iterative route construction and improvement algorithm for the vehicle

routing problem with soft time windows. Transportation Research Part C: Emerging Tech-

nologies 18(5) 668–679.

Fontana, M.W. 2013. Optimal routes for electric vehicles facing uncertainty, congestion, and

energy constraints. Ph.D. thesis, Massachusetts Institute of Technology. URL http://

dspace.mit.edu/bitstream/handle/1721.1/84715/868238531.pdf?sequence=1. Last ac-

cessed 18/6/2014.

Goncalves, F., S.R. Cardoso, S. Relvas, A.P.F.D. Barbosa-Povoa. 2011. Optimization of a distribu-

tion network using electric vehicles: A VRP problem. Technical report, CEG-IST, Technical

University of Lisbon, Portugal.

Gonzalez-Feliu, J., F. Semet, J-L. Routhier, eds. 2014. Sustainable urban logistics: Concepts,

methods and information systems. Springer, Berlin Heidelberg.

Haghbin, S., K. Khan, S. Lundmark, M. Alakula, O. Carlson, M. Leksell, O. Wallmark. 2010.

Integrated chargers for EV’s and PHEV’s: Examples and new solutions. IEEE 2010 XIX

International Conference on Electrical Machines (ICEM). IEEE, Rome, 1–6.

Hannisdahl, O.H., H.V. Malvik, G.B. Wensaas. 2013. The future is electric! The

EV revolution in Norway - explanations and lessons learned. Paper presented at


28 CIRRELT-2014-44

the EVS27 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium.

Barcelona. URL http://www.gronnbil.no/getfile.php/FILER/Norway%20-%20lessons%

20learned%20from%20a%20global%20EV%20success%20story%20-%20Final.pdf. Last ac-

cessed 10/6/2014.

Hatton, C.E., S.K. Beella, J.C. Brezet, Y. Wijnia. 2009. Charging stations for urban settings -

The design of a product platform for electric vehicle infrastructure in Dutch cities. Paper pre-

sented at the EVS24 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium.

Stavanger, Norway. URL http://www.e-mobile.ch/pdf/2010/EVS-24-1230095.pdf. Last

accessed 2/5/2014.

Hazeldine, T., S. Kollamthodi, C. Brannigan, M. Morris, L. Deller. 2009. Market outlook

to 2022 for battery electric vehicles and plug-in hybrid electric vehicles. AEA Group,

commissionned by the Committee on Climate Change, Oxfordshire, England. URL

http://www.ricardo-aea.com/cms/assets/Uploads/Papers-and-Reports/Sustainable-

Transport/AEA-Market-outlook-to-2022-for-battery-electric-vehicles-and-plug-

in-hybrid-electric-vehicles-1.pdf. Last accessed 7/5/2014.

Idaho National Laboratory. 2014. DC fast charging effects on battery life and evse efficiency and

security testing. Presentation given at the 2014 U.S Department of Energy Hydrogen and Fuel

Cells Program and Vehicle Technologies Office Annual Merit Review and Peer Evaluation

Meeting. URL http://energy.gov/sites/prod/files/2014/07/f18/vss131_francfort_

2014_o.pdf. Last accessed 11/8/2014.

International Energy Agency, Electric Vehicles Initiative. 2013. Global EV outlook - Under-

standing the electric vehicle landscape to 2020. URL http://www.iea.org/publications/

globalevoutlook_2013.pdf. Last accessed 12/5/2014.

Jerram, L., J. Gartner. 2013. Report executive summary - Hybrid electric, plug-in hybrid, and

battery electric light duty, medium duty, and heavy duty trucks and vans: Global market

analysis and forecasts. Navigant Research. URL http://www.navigantresearch.com/wp-

assets/uploads/2013/12/HTKS-13-Executive-Summary.pdf. Last accessed 5/6/2014.

Khaligh, A., Z. Li. 2010. Battery, ultracapacitor, fuel cell, and hybrid energy storage systems for

electric, hybrid electric, fuel cell, and plug-in hybrid electric vehicles: State of the art. IEEE

Transactions on Vehicular Technology 59(6) 2806–2814.

Kobayashi, Y., N. Kiyama, H. Aoshima, M. Kashiyama. 2011. A route search method for electric

vehicles in consideration of range and locations of charging stations. 2011 IEEE Intelligent

Vehicles Symposium (IV). IEEE, Baden-Baden, Germany, 920–925.

Koc, C., T. Bektas, O. Jabali, G. Laporte. 2014. The fleet size and mix pollution-routing problem.

Transportation Research, Part B . Forthcoming.

Kopfer, H.W., H. Kopfer. 2013. Emissions minimization vehicle routing in dependence of different


CIRRELT-2014-44 29

vehicle classes. Kreowski, H.-J., Reiter, B.S., Thoben, K.-D.,(Eds). Dynamics in Logistics,

Lecture Notes in Logistics. Springer, Berlin, 49–58.

Kopfer, H.W., J. Schonberger, H. Kopfer. 2014. Reducing greenhouse gas emissions of a heteroge-

neous vehicle fleet. Flexible Services and Manufacturing Journal 26 221–248.

Larminie, J., J. Lowry. 2003. Electric Vehicle Technology Explained . Wiley, Chichester.

Lee, D.Y., V.M. Thomas, M.A. Brown. 2013. Electric urban delivery trucks: Energy use, greenhouse

gas emissions, and cost-effectiveness. Environmental science & technology 47(14) 8022–8030.

Lin, C., K.L. Choy, G.T.S. Ho, S.H. Chung, H.Y. Lam. 2014. Survey of green vehicle routing

problem: Past and future trends. Expert Systems with Applications 41(4) 1118–1138.

Lukic, S.M., J. Cao, R.C. Bansal, F. Rodriguez, A. Emadi. 2008. Energy storage systems for

automotive applications. IEEE Transactions on Industrial Electronics 55(6) 2258–2267.

MacLean, H.L., L.B. Lave. 2003. Evaluating automobile fuel/propulsion system technologies.

Progress in Energy and Combustion Science 29(1) 1–69.

Mak, H.Y., Y. Rong, Z.J.M. Shen. 2013. Infrastructure planning for electric vehicles with battery

swapping. Management Science 59(7) 1557–1575.

McMorrin, F., R. Anderson, I. Featherstone, C. Watson. 2012. Plugged-in fleets: A guide to de-

ploying electric vehicles in fleets. The Climate Group, Cenex, and Energy Saving Trust. URL

http://www.theclimategroup.org/_assets/files/EV_report_final_hi-res.pdf. Last

accessed 27/5/2014.

MDS Transmodal Limited. 2012. DG move - European Commission: Study on urban

freight transport. In association with Centro di ricerca per il Trasporto e la Logis-

tica (CTL). URL http://ec.europa.eu/transport/themes/urban/studies/doc/2012-

04-urban-freight-transport.pdf. Last accessed 3/7/2014.

Millner, A. 2010. Modeling lithium ion battery degradation in electric vehicles. 2010 IEEE Con-

ference on Innovative Technologies for an Efficient and Reliable Electricity Supply (CITRES).

IEEE, 349–356.

Mock, P., Z. Yang. 2014. Driving electrification: A global comparison of fiscal incen-

tive policy for electric vehicles. The International Council on Clean Transportation

(ICCT). URL http://www.theicct.org/sites/default/files/publications/ICCT_EV-

fiscal-incentives_20140506.pdf. Last accessed 22/6/2014.

Montoya, A., C. Gueret, J.E. Mendoza, J.G. Villegas. 2014. A modified multi space sampling heuris-

tic for the green vehicle routing problem. LARIS-EA -7315 Universite d’Angers, France. URL

http://lisaref.univ-angers.fr/depot/2014/LARIS20141.pdf. Last accessed 30/8/2014.

Mosquet, X., M. Devineni, T. Mezger, H. Zablit, A. Dinger, G. Sticher, M. Gerrits, M. Russo. 2011.

Powering autos to 2020 - The era of the electric car? The Boston Consulting Group. URL

http://www.bcg.com/documents/file80920.pdf. Last accessed 20/5/2014.


30 CIRRELT-2014-44

Naberezhnykh, D., J J. Wardle, J J. Lowes, C. Herron, T. Brightman, T. Parker. 2012b. Electric

vehicle charging points for freight vehicles in central London (Version - Draft 0.7). Prepared

for Central London FQP by Transport & Travel Research Ltd, in partnership with TRL and

Zero Carbon Futures. URL http://www.centrallondonfqp.org/app/download/12240926/

CLFQP_EVCP_strategy+report_Final+v1+0.pdf. Last accessed 9/6/2014.

Nesterova, N., H. Quak, S. Balm, I. Roche-Cerasi, T. Tretvik. 2013. Project FREVUE

deliverable D1.3: State of the art of the electric freight vehicles implementation in

city logistics. TNO and SINTEF. European Commission Seventh framework pro-

gramme. URL http://frevue.eu/wp-content/uploads/2014/05/FREVUE-D1-3-State-

of-the-art-city-logistics-and-EV-final-.pdf. Last accessed 19/5/2014.

New York State Energy Research and Development Authority. 2014. New York truck voucher

incentive program - NYSEV-VIF all-electric vehicle eligibility list. URL https://truck-

vip.ny.gov/NYSEV-VIF-vehicle-list.php. Last accessed 28/6/2014.

Offer, G.J., D. Howey, M. Contestabile, R. Clague, N.P. Brandon. 2010. Comparative analysis of

battery electric, hydrogen fuel cell and hybrid vehicles in a future sustainable road transport

system. Energy Policy 38(1) 24–29.

Omidvar, A., R. Tavakkoli-Moghaddam. 2012. Sustainable vehicle routing: Strategies for conges-

tion management and refueling scheduling. 2012 IEEE International Energy Conference and

Exhibition (ENERGYCON). IEEE, 1089–1094.

Parish, R., W. Pitkanen. 2012. Demand assessment of first-mover hybrid and electric truck

fleets. CALSTART. URL http://www.calstart.org/Libraries/Publications/Demand_

Assessment_of_First-Mover_Hybrid_and_Electric_Truck_Fleets.sflb.ashx. Last ac-

cessed 8/6/2014.

Pitkanen, W., B. Van Amburg. 2012. Best fleet uses, key challenges and the early busi-

ness case for e-trucks: Findings and recommendations of the e-truck task force.

CALSART. URL http://www.calstart.org/Libraries/E-Truck_Task_Force_

Documents/Best_Fleet_Uses_Key_Challenges_and_the_Early_Business_Case_for_E-

Trucks_Findings_and_Recommendations_of_the_E-Truck_Task_Force.sflb.ashx. Last

accessed 11/6/2014.

Pollet, B.G., I. Staffell, J.L. Shang. 2012. Current status of hybrid, battery and fuel cell electric

vehicles: From electrochemistry to market prospects. Electrochimica Acta 84 235–249.

Psaraftis, H.N. 2014. Green transportation in logistics: The quest for win-win solutions. Springer,

Berlin Heidelberg. Forthcoming.

Prud’homme, R., M. Koning. 2012. Electric vehicles: A tentative economic and environmental

evaluation. Transport Policy 23 60–69.

Sachenbacher, M., M. Leucker, A. Artmeier, J. Haselmayr. 2011. Efficient energy-optimal routing

for electric vehicles. 25th Association for the Advancement of Artificial Intelligence (AAAI)


CIRRELT-2014-44 31

Conference. San Francisco, California, 1402–1407. URL http://www.aaai.org/ocs/index.

php/AAAI/AAAI11/paper/download/3735/4092. Last accessed 14/7/2014.

Schneider, M., A. Stenger, D. Goeke. 2014. The electric vehicle-routing problem with time windows

and recharging stations. Transportation Science doi = 10.1287/trsc.2013.0490)

Siddiqi, U.F., Y. Shiraishi, S.M. Sait. 2011a. Multi-constrained route optimization for electric vehi-

cles (EVs) using particle swarm optimization (PSO). IEEE 2011 11th International Conference

on Intelligent Systems Design and Applications (ISDA). IEEE, 391–396.

Siddiqi, U.F., Y. Shiraishi, S.M. Sait. 2011b. Multi-constrained route optimization for electric ve-

hicles using SimE. 2011 International Conference of Soft Computing and Pattern Recognition

(SoCPaR). IEEE, 376–383.

Siddiqi, U.F., Y. Shiraishi, S.M. Sait. 2012. Multi-objective optimal path selection in electric vehi-

cles. 17th International Symposium on Artificial Life and Robotics. Springer, Oita, Japan, 113–

122. URL http://www.researchgate.net/publication/257469180_Multi-objective_

optimal_path_selection_in_electric_vehicles/file/3deec52849d03f1009.pdf. Last

accessed 2/8/2014.

Sierzchula, W., S. Bakker, K. Maat, B. van Wee. 2012. The competitive environment of electric

vehicles: An analysis of prototype and production models. Environmental Innovation and

Societal Transitions 2 49–65.

Sweda, T.M., I.S. Dolinskaya, D. Klabjan. 2014. Optimal recharging policies for electric vehicles.

Working paper No.14-01, Department of Industrial Engineering and Management Sciences,

Northwestern University. URL http://www.iems.northwestern.edu/docs/WP_14-01.pdf.


Sweda, T.M., D. Klabjan. 2012. Finding minimum-cost paths for electric vehicles. 2012 IEEE

International Electric Vehicle Conference (IEVC). IEEE. URL http://dynresmanagement.

com/uploads/3/3/2/9/3329212/charging_ievc.pdf. Last accessed 10/6/2014.

Taefi, T., J. Kreutzfeldt, T. Held, R. Konings, R. Kotter, S. Lilley, H. Baster, N. Green, M. Stie

Laugesen, S. Jacobsson, M. Borgqvist, C. Nyquist. 2014. Comparative analysis of european

examples of freight electric vehicles schemes. A systematic case study approach with examples

from Denmark, Germany, the Netherlands, Sweden and the UK. 4th International Conference

on Dynamics in Logistics (LDIC 2014). Bremen, Germany. URL http://nrl.northumbria.

ac.uk/15185/1/Bremen_final_paperShoter.pdf. Last accessed 10/7/2014.

Taha, M., M.N. Fors, A.A. Shoukry. 2014. An exact solution for a class of green vehicle routing

problem. Proceedings of the 2014 International Conference on Industrial Engineering and

Operations Management . Bali, Indonesia, 1383–1390. URL http://iieom.org/ieom2014/

pdfs/306.pdf. Last accessed 16/7/2014.

Thiel, C., A. Perujo, A. Mercier. 2010. Cost and CO2 aspects of future vehicle options in Europe

under new energy policy scenarios. Energy Policy 38(11) 7142–7151.


32 CIRRELT-2014-44

TU Delft, HAW Hamburg, Lindholmen Science Park, ZERO, FDT. 2013. Comparative analysis of

european examples of schemes for freight electric vehicles - Compilation report. E-Mobility

NSR, Aalborg, Denmark. URL http://e-mobility-nsr.eu/fileadmin/user_upload/

downloads/info-pool/E-Mobility_-_Final_report_7.3.pdf. Last accessed 3/7/2014.

Tuttle, D.P., K.M. Kockelman. 2012. Electrified vehicle technology trends, infrastructure impli-

cations, and cost comparisons. Journal of the Transportation Research Forum 51(1) 35–51.

URL http://journals.oregondigital.org/trforum/article/view/2806/2411. Last ac-

cessed 18/8/2014.

U.S. Department of Energy. 2010. The recovery act: Transforming America’s transportation sec-

tor - batteries and electric vehicles. URL http://www.whitehouse.gov/files/documents/

Battery-and-Electric-Vehicle-Report-FINAL.pdf. Last accessed 20/5/2014.

U.S. Department of Energy. 2012a. All laws and incentives sorted by type. Office of Energy

Efficiency and Renewable Energy, Alternative Fuels Data Center. URL http://www.afdc.

energy.gov/laws/matrix/incentive. Last accessed 2/6/2014.

U.S. Department of Energy. 2012b. Plug-in electric vehicle handbook for fleet managers. Office of

Energy Efficiency and Renewable Energy, National Renewable Energy Laboratory (NREL).

URL http://www.afdc.energy.gov/pdfs/pev_handbook.pdf. Last accessed 10/5/2014.

U.S. Department of Energy. 2013a. Clean cities guide to alternative fuel and advanced medium- and

heavy-duty vehicles. Office of Energy Efficiency and Renewable Energy, National Renewable

Energy Laboratory (NREL). URL http://www.afdc.energy.gov/uploads/publication/

medium_heavy_duty_guide.pdf. Last accessed 3/6/2014.

U.S. Department of Energy. 2013b. Vehicle technologies program - Smith Newton vehicle perfor-

mance evaluation. URL http://www.nrel.gov/docs/fy13osti/58108.pdf. Last accessed

13/6/2014.

U.S. Department of Energy. 2014b. Vehicle weight classes & categories. Office of Energy Efficiency

and Renewable Energy, Alternative Fuels Data Center. URL http://www.afdc.energy.gov/

data/10380. Last accessed 12/7/2014.

Valenta, M. 2013. Business case of electric vehicles for truck fleets. Ph.D. thesis, Argosy Univer-

sity, Denver, Colorado. URL http://www.onthegreenllc.net/uploads/3/3/8/0/3380113/

valenta_dissertation_published.pdf. Last accessed 12/6/2014.

van Duin, J.H.R., H. Quak, J. Muuzuri. 2010. New challenges for urban consolidation centres: A

case study in the Hague. Procedia-Social and Behavioral Sciences 2(3) 6177–6188.

van Duin, J.H.R., L.A. Tavasszy, H.J. Quak. 2013. Towards e(lectric)-urban freight: first promising

steps in the electric vehicle revolution. European Transport / Trasporti Europei 54(9) 1–

19. URL http://www.openstarts.units.it/dspace/bitstream/10077/8875/1/ET_2013_

54_9%20van%20Duin%20et%20al..pdf. Last accessed 6/7/2014.


CIRRELT-2014-44 33

Vermie, T. 2002. ELCIDIS - electric vehicle city distribution final report. European Commission.

URL http://www.elcidis.org/elcidisfinal.pdf. Last accessed 28/5/2014.

Yılmaz, M., P.T. Krein. 2013. Review of battery charger topologies, charging power levels, and

infrastructure for plug-in electric and hybrid vehicles. IEEE Transactions on Power Electronics

28(5) 2151–2169.

Zhang, S.S. 2006. The effect of the charging protocol on the cycle life of a Li-ion battery. Journal

of Power Sources 161(2) 1385–1391.


34 CIRRELT-2014-44